基于python的DE-GWO算法代码

以下是基于Python的DE-GWO算法代码：

import numpy as np

# 定义目标函数（这里以Rastrigin函数为例）
def objective_function(x):
    return 10 * len(x) + np.sum(x**2 - 10 * np.cos(2 * np.pi * x))

# 定义DE算法
def differential_evolution(fobj, bounds, popsize, mutate, recombination, maxiter):
    # 初始化种群
    pop = np.random.rand(popsize, len(bounds))
    min_b, max_b = np.asarray(bounds).T
    diff = np.fabs(min_b - max_b)
    pop_denorm = min_b + pop * diff
    # 评估初始种群
    fitness = np.asarray([fobj(ind) for ind in pop_denorm])
    best_idx = np.argmin(fitness)
    best = pop_denorm[best_idx]
    # 迭代
    for i in range(maxiter):
        for j in range(popsize):
            idxs = list(range(popsize))
            idxs.remove(j)
            a, b, c = pop[np.random.choice(idxs, 3, replace=False)]
            mutant = a + mutate * (b - c)
            mutant = np.clip(mutant, 0, 1)
            cross_points = np.random.rand(len(bounds)) < recombination
            if not np.any(cross_points):
                cross_points[np.random.randint(0, len(bounds))] = True
            trial = np.where(cross_points, mutant, pop[j])
            trial_denorm = min_b + trial * diff
            f = fobj(trial_denorm)
            if f < fitness[j]:
                fitness[j] = f
                pop[j] = trial
                if f < fitness[best_idx]:
                    best_idx = j
                    best = trial_denorm
        # 使用GWO算法更新参数
        a = 2 - 2 * i / maxiter
        A = 2 * a * np.random.rand(len(bounds)) - a
        C = 2 * np.random.rand(len(bounds))
        l = np.random.rand()
        p = np.random.rand()
        for j in range(popsize):
            D_alpha = np.abs(C * best - pop[j])
            X1 = best - A * D_alpha
            D_beta = np.abs(C * pop[best_idx] - pop[j])
            X2 = pop[best_idx] - A * D_beta
            D_delta = np.abs(C * pop[j] - pop[best_idx])
            X3 = pop[j] - A * D_delta
            new_sol = (X1 + X2 + X3) / 3
            new_sol_denorm = min_b + new_sol * diff
            new_fitness = fobj(new_sol_denorm)
            if new_fitness < fitness[j]:
                fitness[j] = new_fitness
                pop[j] = new_sol
                if new_fitness < fitness[best_idx]:
                    best_idx = j
                    best = new_sol_denorm
    return best, fitness[best_idx]

# 测试DE-GWO算法
bounds = [(-5.12, 5.12)] * 30
result = differential_evolution(objective_function, bounds, popsize=30, mutate=0.5, recombination=0.7, maxiter=1000)
print('最优解：', result[0])
print('最优函数值：', result[1])

DE-GWO算法的主要思想是将Differential Evolution (DE)算法与Grey Wolf Optimization (GWO)算法相结合，其中DE算法用于搜索解空间，GWO算法用于更新参数。在每次迭代中，DE算法搜索新的解，然后GWO算法根据当前最优解和最劣解来更新参数。通过这种方式，DE-GWO算法能够克服DE算法易陷入局部最优解和GWO算法难以处理高维优化问题的缺点，从而提高了搜索效率。